CN117950016A - Stratum model construction method and device based on seismic dip angle attribute and electronic equipment - Google Patents
Stratum model construction method and device based on seismic dip angle attribute and electronic equipment Download PDFInfo
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Abstract
The invention discloses a stratum model construction method and device based on seismic dip angle attributes and electronic equipment, wherein the method comprises the following steps: step S1: reading the stacked seismic data; step S2: calculating dip angle attribute of each sample point in the seismic data; step S3: tracking horizon data from the seismic data based on the dip angle attribute, and obtaining N horizon planes which do not intersect with each other according to the horizon data; step S4: and constructing a stratum model based on the N horizon planes. The method and the device can improve the accuracy of stratum modeling.
Description
Technical Field
The invention belongs to the field of oil and gas geophysical exploration, and particularly relates to a stratum model construction method and device based on seismic dip angle attributes and electronic equipment.
Background
The existing stratum model building technology is based on limited geological data in space, and then the geological data of the full-work area, such as elevation values of the stratum surface, are obtained through a mathematical interpolation algorithm. The formation model built in hydrocarbon geophysics is based on wave impedance, i.e. the uniform wave impedance is considered as a formation, and the wave impedance boundary is the boundary of the formation, so that the formation model in hydrocarbon geophysics is built consistent with the formation model building method in geological engineering, i.e. the well wave impedance model is built firstly, the wells are generally unevenly distributed in space positions and the number of the wells is small, the elevation value of one formation surface is picked up from each well, the elevation values picked up from all the wells form a plurality of discrete values on the space formation surface, and a space curved surface is obtained by using a mathematical interpolation algorithm such as a dem interpolation algorithm, and the curved surface is the built formation model. And after all the strata are calculated according to the method, the strata are ordered according to the relative geologic time, and all the strata form a stratum model.
The formation model is usually constructed by model driving, namely, the situation of spreading of the stratum in the ground in space is made according to the prior experience of geology, the method is suitable for the situation that the transverse stratum changes are not obvious, and when the transverse transformation of the geology is relatively fast, the stratum modeling precision of a model driving method is greatly reduced.
Disclosure of Invention
The invention aims to provide a stratum model construction method and device based on seismic dip angle attributes and electronic equipment, and the accuracy of stratum modeling is improved.
In a first aspect, the present invention provides a method for constructing a stratum model based on seismic dip attributes, including:
Step S1: reading the stacked seismic data;
step S2: calculating dip angle attribute of each sample point in the seismic data;
Step S3: tracking horizon data from the seismic data based on the dip angle attribute, and obtaining N horizon planes which do not intersect with each other according to the horizon data;
step S4: and constructing a stratum model based on the N horizon planes.
Optionally, the step S2 includes:
calculating dip angle attributes of wave crest and wave trough sample points in the seismic data through dip angle attribute tensors;
And calculating dip angle attributes of non-wave crest and wave trough sampling points in the seismic data by an interpolation method.
Optionally, the calculating the dip angle attribute of the peak and trough sampling points in the seismic data through the dip angle attribute tensor includes:
The expression formula for setting the sample point value in the seismic data is as follows:
u＝u(x,t) (1)
Wherein u is the vibration intensity of the seismic signal, and x and t are the seismic trace number sequence and the time sequence respectively;
tensor of dip properties constructed:
Wherein, Q tt、qtx、qxt、qxx is the tensor component of the dip attribute;
And (3) carrying out eigenvalue eigenvector decomposition on the formula (2) to obtain: lambda i,vi (i=1, 2), which respectively correspond to a set of eigenvalues and eigenvectors;
And taking the feature vector corresponding to the smaller feature value as the inclination angle direction of the corresponding seismic data sample point.
Optionally, the calculating dip properties of non-peak and valley samples in the seismic data by interpolation is calculated by the following formula:
Wherein q j is the dip attribute of data sample point j, q 1 is the dip attribute of data sample point 1, q 2 is the dip attribute of data sample point 2, and data sample point j is between data sample point 1 and data sample point 2.
Optionally, the step S3 includes:
step S301: selecting an earthquake channel from the earthquake section of the earthquake data to mark N extreme points corresponding to the positions of all wave crests and wave troughs, and marking the N extreme points as: p 1,p2....pN;
Step S302: tracking horizon data h 1,h2....hN from the seismic data by taking an extreme point p 1,p2....pN as a seed point and taking an inclination angle attribute as a tracking basis;
Step S303: based on the optimal horizon criterion, selecting m optimal horizons from horizon data h 1,h2....hN, wherein m is more than or equal to 1;
Step S304: splitting the seismic data into m+1 parts, and repeating the calculation process of steps S301-S303 for each split part of the seismic data until any point in p 1,p2....pN is not contained in the split seismic data, so as to obtain N optimal horizon planes, wherein the N horizon planes are not intersected with each other.
Optionally, the expression of the optimal horizon criterion is:
Where σ=1, m is the number of traces of the seismic data, va is the objective function, and the larger the value is, the better the horizon is, and t i and t j are the time elevation values of the horizons at different positions.
Optionally, the step S4 includes:
initializing a new seismic data volume, and marking an attribute value of a data sample point as 0, wherein the attribute value comprises a corresponding track position, a time position in the longitudinal direction and a relative stratum sequence number;
and (3) projecting each sample point on the N horizon planes obtained in the step (S3) onto a new seismic data body, and setting the sample point attribute to be a numerical value of a relative stratum sequence number.
In a second aspect, the present invention provides a device for constructing a stratum model based on a seismic dip attribute, including:
The data reading module is used for reading the stacked seismic data;
the dip angle attribute calculation module is used for calculating dip angle attributes of each sample point in the seismic data;
The horizon surface tracking module is used for tracking horizon data from the seismic data based on the dip angle attribute and obtaining N horizon surfaces which are not intersected with each other according to the horizon data;
and the stratum construction module is used for constructing a stratum model based on the N horizon planes.
In a third aspect, the present invention proposes an electronic device comprising:
at least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the formation model construction method of any one of the first aspects.
In a fourth aspect, the present invention proposes a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the formation model construction method according to any one of the first aspects.
The invention has the beneficial effects that:
The method directly adopts the seismic data driving method, obtains the stratum model on the basis of the global dip angle attribute, is more suitable for the situation of severe transverse stratum change compared with the geological model driving method, has the advantages that the stratum model trend established by the method is consistent with the trend of the seismic data, can accurately reflect the trend of the underground structure, and effectively improves the stratum modeling precision.
The system of the present invention has other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the invention.
Drawings
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the invention.
FIG. 1 shows a step diagram of a method for constructing a stratigraphic model based on seismic dip attributes in accordance with the present invention.
FIG. 2 shows a raw seismic data map according to one embodiment of the invention.
Fig. 3 shows an optimal horizon map obtained in a method for constructing a horizon model based on seismic dip properties according to the invention.
Fig. 4 shows a stratigraphic model diagram obtained in a method for constructing a stratigraphic model based on seismic dip attributes according to the invention.
Detailed Description
The invention will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present invention are illustrated in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Example 1
As shown in fig. 1, a method for constructing a stratum model based on seismic dip angle attributes includes:
Step S1: reading the stacked seismic data;
step S2: calculating dip angle attribute of each sample point in the seismic data;
the method comprises the following steps of calculating the global space seismic dip angle attribute:
calculating dip angle attributes of wave crest and wave trough sample points in the seismic data through dip angle attribute tensors;
And calculating dip angle attributes of non-wave crest and wave trough sampling points in the seismic data by an interpolation method.
Specifically, the method for calculating the global dip angle of the earthquake adopts two directions to calculate respectively, namely an inline direction and a crossline direction of the post-stack data of the earthquake, and the following description uses the inline direction as an example to obtain the attribute of the global space earthquake dip angle.
In the inline direction, the seismic data is a 2.5-dimensional image, namely, the longitudinal direction, the time direction and the transverse direction of the seismic channel, each sample value on the grid is the intensity of the vibration of the seismic signal, and the image is expressed by using colors.
The mathematical expression for the sample values may be:
u＝u(x,t) (1)
Where u is the vibration intensity of the seismic signal, and x and t are the sequence of seismic trace numbers and the time sequence, respectively.
Then, the process is carried out,
Tensor of dip properties constructed:
Wherein, Q tt、qtx、qxt、qxx is the tensor component of the dip attribute;
And (3) carrying out eigenvalue eigenvector decomposition on the formula (2) to obtain: lambda i,vi (i=1, 2) corresponds to a set of eigenvalues and eigenvectors respectively, here, the eigenvector corresponding to the smaller eigenvalue is taken, the eigenvector corresponds to the dip angle direction of the seismic data sample point, and it is to be noted that the above method only calculates the dip angle attribute of the point through dip angle attribute tensor for the peak and trough points of the seismic data.
When the seismic data sampling points are not peaks and valleys, an interpolation method is adopted to obtain dip angle attributes, and the specific method is realized by the following interpolation formula:
the data sample point j is between the sample points 1 and 2, and the inclination angle attributes corresponding to the three sample points are respectively as follows: q j,q1,q2.
And calculating the dip angle attribute of all the seismic data sample points by the method.
Step S3: tracking horizon data from the seismic data based on the dip angle attribute, and obtaining N horizon planes which do not intersect with each other according to the horizon data;
specifically, when the seismic dip angle attribute of all the sample points is calculated, the formation of the stratum model is started immediately, and the specific flow is as follows:
step S301: selecting a position of a wave crest and a wave trough of a seismic channel from the seismic section, and marking the position as N extreme points on the assumption: p 1,p2....pN;
step S302: and (3) tracking horizon data h 1,h2....hN by taking the initial extreme points as seed points and the dip angle attribute of the sample points obtained in the step S2 as tracking basis.
Step S303: selecting m optimal layers based on the criteria of the optimal layers, wherein m is more than or equal to 1, and the criteria of the optimal layers are as follows:
where σ=1, m is the number of traces of the seismic data, va is the objective function, the larger this value indicates the better the horizon, t i and t j are the time elevation values of the horizons at different locations;
The best one of the horizons is selected as: h b.
Step S304: for one piece of seismic data, if one piece of horizon data is obtained, the seismic data is divided into 2 independent parts, if 3 pieces of horizon data are obtained, the seismic data is divided into 4 parts, and the like, namely the seismic data is divided into m+1 parts, and the calculation process of steps S301-S303 is repeated for each divided part of seismic data.
For example, after obtaining an optimal horizon, the seismic data is divided into 2 parts, s1 and s2 are recorded, s1 is taken as new data separately, and the process (1) (2) is repeated, and s2 is the same process until the decomposed seismic data does not contain any point p 1,p2....pN. Therefore, N optimal horizon planes are obtained, the N horizon planes do not cross each other, and the requirement of formation model construction is met.
Step S4: and constructing a stratum model based on the N horizon planes.
The method specifically comprises the following steps:
initializing a new seismic data volume, and marking an attribute value of a data sample point as 0, wherein the attribute value comprises a corresponding track position, a time position in the longitudinal direction and a relative stratum sequence number;
and (3) projecting each sample point on the N horizon planes obtained in the step (S3) onto a new seismic data body, and setting the sample point attribute to be a numerical value of a relative stratum sequence number.
Specifically, the present step is formation model construction. After a set of N optimal horizon planes is generated, the horizon planes need to be ordered according to relative horizon sequences. Providing that the seismic data has n seismic channels in total, initializing a new seismic data body, wherein the data size is consistent with that of the original seismic data, marking the attribute value of the data sample point as 0, projecting the horizon onto the new seismic data body, the attribute is a numerical value of relative stratum sequence number, and marking the initialized new seismic data as three characteristics of each sample point, namely the corresponding channel position, the time position in the longitudinal direction and the relative stratum sequence number: s=s (x, t, rt), where x, t, rt are the sequence number of the trace, the time sequence number, the relative formation sequence number, and the value > =1, s is the sample point, and for any new sample point sp in the seismic data, the corresponding method of determining the relative formation sequence number is:
tr'＝arg min(t'-t) (5)
where t' is the time sequence of sp, t is the time sequence of all s, and sp and s have the same track number and time sequence number.
Thus, the formation model is constructed.
In a specific application example of the embodiment, fig. 2 is original seismic amplitude data, and the dip angle attribute of the extreme points on the seismic trace, that is, dip angle attribute values of the peaks and the troughs, is calculated on the data, so as to obtain dip angle attribute values of all seismic data sample points; FIG. 3 is a schematic diagram of a calculated optimal horizon sequence; FIG. 3 is a model of a formation based on global dip properties using the method of the present embodiment. As can be seen by comparing fig. 3 and 1, the calculated formation model is consistent with the construction exhibited by the original seismic data.
It can be seen that, compared with the geologic model driving method, the method directly adopts the earthquake data driving method, the method is more suitable for the situation of severe transverse stratum change, the established stratum model trend is consistent with the earthquake data trend, and the trend of the underground structure is reflected more accurately.
Example 2
The embodiment provides a stratum model construction device based on seismic dip angle attributes, which comprises:
The data reading module is used for reading the stacked seismic data;
the dip angle attribute calculation module is used for calculating dip angle attributes of each sample point in the seismic data;
The horizon surface tracking module is used for tracking horizon data from the seismic data based on the dip angle attribute and obtaining N horizon surfaces which are not intersected with each other according to the horizon data;
and the stratum construction module is used for constructing a stratum model based on the N horizon planes.
In this embodiment, the inclination attribute calculating module is specifically configured to:
calculating dip angle attributes of wave crest and wave trough sample points in the seismic data through dip angle attribute tensors;
And calculating dip angle attributes of non-wave crest and wave trough sampling points in the seismic data by an interpolation method.
Calculating the dip angle attribute of the wave crest and the wave trough sampling points in the seismic data through the dip angle attribute tensor, wherein the dip angle attribute comprises:
The expression formula for setting the sample point value in the seismic data is as follows:
u＝u(x,t) (1)
Wherein u is the vibration intensity of the seismic signal, and x and t are the seismic trace number sequence and the time sequence respectively;
Tensors of the constructed dip angle attributes;
Wherein,
And (3) carrying out eigenvalue eigenvector decomposition on the formula (2) to obtain: lambda i,vi (i=1, 2), which respectively correspond to a set of eigenvalues and eigenvectors;
And taking the feature vector corresponding to the smaller feature value as the inclination angle direction of the corresponding seismic data sample point.
Calculating dip angle attributes of non-wave crest and wave trough sample points in the seismic data by an interpolation method, wherein the dip angle attributes are calculated by the following formula:
Wherein q j is the dip attribute of data sample point j, q 1 is the dip attribute of data sample point 1, q 2 is the dip attribute of data sample point 2, and data sample point j is between data sample point 1 and data sample point 2.
In this embodiment, the method for tracking horizon data from the seismic data by the horizon surface tracking module based on the dip angle attribute includes:
step S301: selecting an earthquake channel from the earthquake section of the earthquake data to mark N extreme points corresponding to the positions of all wave crests and wave troughs, and marking the N extreme points as: p 1,p2....pN;
Step S302: tracking horizon data h 1,h2....hN from the seismic data by taking an extreme point p 1,p2....pN as a seed point and taking an inclination angle attribute as a tracking basis;
Step S303: based on the optimal horizon criterion, selecting m optimal horizons from horizon data h 1,h2....hN, wherein m is more than or equal to 1;
Step S304: splitting the seismic data into m+1 parts, and repeating the calculation process of steps S301-S303 for each split part of the seismic data until any point in p 1,p2....pN is not contained in the split seismic data, so as to obtain N optimal horizon planes, wherein the N horizon planes are not intersected with each other.
The expression of the optimal horizon criterion is as follows:
Where σ=1, m is the number of traces of the seismic data, va is the greater the value, the better the horizon, t i is, and t j is.
In this embodiment, the method for constructing a stratum model by the stratum construction module based on N horizon planes includes:
initializing a new seismic data volume, and marking an attribute value of a data sample point as 0, wherein the attribute value comprises a corresponding track position, a time position in the longitudinal direction and a relative stratum sequence number;
And projecting each sample point on the obtained N horizon planes onto a new seismic data body, and setting the sample point attribute to be a numerical value of a relative stratum sequence number.
Specifically, after a set of N optimal horizon planes is generated, the horizon planes need to be ordered according to the relative horizon order. Providing that the seismic data has n seismic channels in total, initializing a new seismic data body, wherein the data size is consistent with that of the original seismic data, marking the attribute value of the data sample point as 0, projecting the horizon onto the new seismic data body, the attribute is a numerical value of relative stratum sequence number, and marking the initialized new seismic data as three characteristics of each sample point, namely the corresponding channel position, the time position in the longitudinal direction and the relative stratum sequence number: s=s (x, t, rt), where x, t, rt are the sequence number of the trace, the time sequence number, the relative formation sequence number, and the value > =1, s is the sample point, and for any new sample point sp in the seismic data, the corresponding method of determining the relative formation sequence number is:
tr'＝arg min(t'-t) (5)
where t' is the time sequence of sp, t is the time sequence of all s, and sp and s have the same track number and time sequence number.
Example 3
The present embodiment provides an electronic device including:
at least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the formation model construction method of embodiment 1.
An electronic device according to an embodiment of the present disclosure includes a memory for storing non-transitory computer-readable instructions and a processor. In particular, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like.
The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device to perform the desired functions. In one embodiment of the present disclosure, the processor is configured to execute the computer readable instructions stored in the memory.
It should be understood by those skilled in the art that, in order to solve the technical problem of how to obtain a good user experience effect, the present embodiment may also include well-known structures such as a communication bus, an interface, and the like, and these well-known structures are also included in the protection scope of the present disclosure.
The detailed description of the present embodiment may refer to the corresponding description in the foregoing embodiments, and will not be repeated herein.
Example 4
The present embodiment provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the formation model construction method described in embodiment 1.
A computer-readable storage medium according to an embodiment of the present disclosure has stored thereon non-transitory computer-readable instructions. When executed by a processor, perform all or part of the steps of the methods of embodiments of the present disclosure described above.
The computer-readable storage medium described above includes, but is not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or removable hard disk), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described.
Claims (10)
1. The stratum model construction method based on the seismic dip angle attribute is characterized by comprising the following steps of:
Step S1: reading the stacked seismic data;
step S2: calculating dip angle attribute of each sample point in the seismic data;
Step S3: tracking horizon data from the seismic data based on the dip angle attribute, and obtaining N horizon planes which do not intersect with each other according to the horizon data;
step S4: and constructing a stratum model based on the N horizon planes.
2. The formation model construction method according to claim 1, wherein the step S2 includes:
calculating dip angle attributes of wave crest and wave trough sample points in the seismic data through dip angle attribute tensors;
And calculating dip angle attributes of non-wave crest and wave trough sampling points in the seismic data by an interpolation method.
3. The method of claim 2, wherein calculating dip properties of peaks and troughs in the seismic data from dip property tensors comprises:
The expression formula for setting the sample point value in the seismic data is as follows:
u＝u(x,t) (1)
Wherein u is the vibration intensity of the seismic signal, and x and t are the seismic trace number sequence and the time sequence respectively;
tensor of dip properties constructed:
Wherein, Q tt、qtx、qxt、qxx is the tensor component of the dip attribute;
And (3) carrying out eigenvalue eigenvector decomposition on the formula (2) to obtain: lambda i,vi (i=1, 2), which respectively correspond to a set of eigenvalues and eigenvectors;
And taking the feature vector corresponding to the smaller feature value as the inclination angle direction of the corresponding seismic data sample point.
4. The method of claim 2, wherein calculating dip properties of non-peak and valley samples in the seismic data by interpolation is calculated by the following formula:
Wherein q j is the dip attribute of data sample point j, q 1 is the dip attribute of data sample point 1, q 2 is the dip attribute of data sample point 2, and data sample point j is between data sample point 1 and data sample point 2.
5. The formation model construction method according to claim 1, wherein the step S3 includes:
step S301: selecting an earthquake channel from the earthquake section of the earthquake data to mark N extreme points corresponding to the positions of all wave crests and wave troughs, and marking the N extreme points as: p 1,p2....pN;
Step S302: tracking horizon data h 1,h2....hN from the seismic data by taking an extreme point p 1,p2....pN as a seed point and taking an inclination angle attribute as a tracking basis;
Step S303: based on the optimal horizon criterion, selecting m optimal horizons from horizon data h 1,h2....hN, wherein m is more than or equal to 1;
Step S304: splitting the seismic data into m+1 parts, and repeating the calculation process of steps S301-S303 for each split part of the seismic data until any point in p 1,p2....pN is not contained in the split seismic data, so as to obtain N optimal horizon planes, wherein the N horizon planes are not intersected with each other.
6. The formation model construction method according to claim 5, wherein the expression of the optimal horizon criterion is:
Where σ=1, m is the number of traces of the seismic data, va is the objective function, and the larger the value is, the better the horizon is, and t i and t j are the time elevation values of the horizons at different positions.
7. The formation model construction method according to claim 1, wherein the step S4 includes:
initializing a new seismic data volume, and marking an attribute value of a data sample point as 0, wherein the attribute value comprises a corresponding track position, a time position in the longitudinal direction and a relative stratum sequence number;
and (3) projecting each sample point on the N horizon planes obtained in the step (S3) onto a new seismic data body, and setting the sample point attribute to be a numerical value of a relative stratum sequence number.
8. A device for constructing a stratigraphic model based on seismic dip attributes, comprising:
The data reading module is used for reading the stacked seismic data;
the dip angle attribute calculation module is used for calculating dip angle attributes of each sample point in the seismic data;
The horizon surface tracking module is used for tracking horizon data from the seismic data based on the dip angle attribute and obtaining N horizon surfaces which are not intersected with each other according to the horizon data;
and the stratum construction module is used for constructing a stratum model based on the N horizon planes.
9. An electronic device, the electronic device comprising:
at least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the formation model construction method of any one of claims 1-7.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the formation model construction method of any one of claims 1-7.
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